ISIS Discovery & Predictive Analytics software is embedded with a full library of  business mathematics to report on the past, analyze the present and predict the future.  At a high level there is:

• Arithmetic
• Mathematics
• Statistics

Arithmetic (add, subtract, multiply and divide) enables comparisons, Mathematics proves a situation of true or false and Statistics is used to calculate the probability of a future outcome.  Arithmetic is the heart of Business Intelligence reporting tools to compare the past to the present.  Mathematic and Statistics are the purview of statistical software and the core of Predictive Analytics.  Business requires all these mathematical components to optimize and assure effective decisions as reports and dashboards alone are insufficient to interpret the status of the business and predict its probable outcomes.

ISIS software has an extensive range of mathematical and statistical formula but you need not have to be a mathematician in order to use their powerful application to data.  Integrated with the ISIS database are 14 key statistics that are on-line all the time to provide insight to the business:

• Variance of Absolute Performance (Over the Selected Time Series)
• Variance of Performance to Plan
• Absolute Duration of a Trend
• Trend Analysis to Plan
• Financial Impact of the Trend
• Slope (Leading Indicator of the Future of the Trend)
• Statistical Process Control Index (Measure of Stability)
• Standard Deviation (Measure of Control)
• Mean (Key Performance Indicator)
• Minimum Value (Over the Selected Time Series)
• Maximum Value (Over the Selected Time Series)
• Correlation (To the Driver of the Business)
• Cost Allocations
• Global Activity Drivers (Normalizing Data for Comparisons)

“Analytics” looks to find the meaning in the data as compared to the reporting, dashboards and scorecards that show and compare historical data.  For example, most businesses know they are losing money on some customers but which ones?  Gross profit is a measure of the relative relation of profitability but to see true profit and loss requires knowing operating income down to the customer and product level.  Without any programming, ISIS can allocate costs to reveal the operating income.  With this knowledge decisions can be made regarding customer management, service levels, deployment of sales resources and marketing channels.  This powerful knowledge leads to increased profitability.

Analytics also helps to bring quantification to the phrase "If it isn't broken don't fix it".  Here Analytics can be applied to determine what is "broken".  For example, is a 1% return on Product X a problem? How about 2% on Product Y? Would 20% be a problem on Product Z? You see a problem (i.e. that which is broken) needs to be relative to a marker and the first marker used to assess the problem is a calculated mean. So, ISIS would calculate the process mean for all products then perform a statistical analysis to calculate the standard deviation from the mean for each product at each point in time.  The Statistical Process Control Index (SPCI) can then be applied to determine the returns on those products that are out of control. Finally, ISIS would dynamically segment the data based on the SPCI into buckets of Best, OK and Problem.  As you can see now, the concept of "broken" requires a bunch of mathematics.  Presented below is a graph of the answer to the question "Where are product returns a problem?"

The figure below is an example of the Statistical Process Control Index that measures returns for a consumer electronic product.  Quality theory tells us that when a process is more than two standard deviations outside its mean value then that process is out of control.  In this example, the gray line measures the standard deviation of the customer return percentage about its black "mean" line.  When the gray line spikes past two standard deviations the process is out of control.  Here, the measurement of customer return percentage can be used as a marker to alert us when our manufacturing is producing results that are out of control.

Another popular need is to compare “apples to apples”.  Here too Analytics can bring quantification and clarification.  Presented on the graph below is a standard business intelligence report showing the supply costs for three departments from which we could conclude Department C was under performing A and B.  Typically, spreadsheets and additional time would be required to discover what's really going on.

With ISIS analytics, the same data as above is present in the figure below, except in this case an “Activity Driver” of Full Time Equivalent (FTE) headcount is applied so that the performance of each of the departments can be normalized against a common economic benchmark. This sample economic driver highlights the efficiency of each department. Instead of Department C appearing as the problem as in the above graph, in the graph below, Department C is actually more efficient in utilizing its resources.  Now correct decisions can be made in comparing the performance between departments.   

ISIS Discovery & Predictive Analytics makes using analytics simple through its English language user interface without the need for programming, spreadsheets or IT support.  User questions are simply answered and users are enabled to dynamically explore and analyze data.